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--- |
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language: |
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- ar |
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license: apache-2.0 |
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tags: |
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- Arabic |
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- Dialect |
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- Egyptian |
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- Gulf |
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- Levantine |
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- Classical Arabic |
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- MSA |
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- Modern Standard Arabic |
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widget: |
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- text: "الهدف من الحياة هو [MASK] ." |
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--- |
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# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks |
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## Model description |
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**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. |
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We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. |
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We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). |
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The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."* |
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This model card describes **CAMeLBERT-Mix** (`bert-base-camelbert-mix`), a model pre-trained on a mixture of these variants: MSA, DA, and CA. |
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||Model|Variant|Size|#Word| |
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|-|-|:-:|-:|-:| |
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|✔|`bert-base-camelbert-mix`|CA,DA,MSA|167GB|17.3B| |
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||`bert-base-camelbert-ca`|CA|6GB|847M| |
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||`bert-base-camelbert-da`|DA|54GB|5.8B| |
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||`bert-base-camelbert-msa`|MSA|107GB|12.6B| |
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||`bert-base-camelbert-msa-half`|MSA|53GB|6.3B| |
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||`bert-base-camelbert-msa-quarter`|MSA|27GB|3.1B| |
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||`bert-base-camelbert-msa-eighth`|MSA|14GB|1.6B| |
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||`bert-base-camelbert-msa-sixteenth`|MSA|6GB|746M| |
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## Intended uses |
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You can use the released model for either masked language modeling or next sentence prediction. |
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However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. |
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We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT). |
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#### How to use |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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>>> from transformers import pipeline |
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>>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-camelbert-mix') |
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>>> unmasker("الهدف من الحياة هو [MASK] .") |
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[{'sequence': '[CLS] الهدف من الحياة هو النجاح. [SEP]', |
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'score': 0.10861027985811234, |
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'token': 6232, |
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'token_str': 'النجاح'}, |
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{'sequence': '[CLS] الهدف من الحياة هو.. [SEP]', |
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'score': 0.07626965641975403, |
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'token': 18, |
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'token_str': '.'}, |
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{'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]', |
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'score': 0.05131986364722252, |
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'token': 3696, |
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'token_str': 'الحياة'}, |
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{'sequence': '[CLS] الهدف من الحياة هو الموت. [SEP]', |
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'score': 0.03734956309199333, |
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'token': 4295, |
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'token_str': 'الموت'}, |
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{'sequence': '[CLS] الهدف من الحياة هو العمل. [SEP]', |
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'score': 0.027189988642930984, |
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'token': 2854, |
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'token_str': 'العمل'}] |
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``` |
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*Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually. |
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Here is how to use this model to get the features of a given text in PyTorch: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-camelbert-mix') |
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model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-camelbert-mix') |
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text = "مرحبا يا عالم." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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and in TensorFlow: |
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```python |
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from transformers import AutoTokenizer, TFAutoModel |
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tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-camelbert-mix') |
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model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-camelbert-mix') |
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text = "مرحبا يا عالم." |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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## Training data |
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- MSA (Modern Standard Arabic) |
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- [The Arabic Gigaword Fifth Edition](https://catalog.ldc.upenn.edu/LDC2011T11) |
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- [Abu El-Khair Corpus](http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus) |
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- [OSIAN corpus](https://vlo.clarin.eu/search;jsessionid=31066390B2C9E8C6304845BA79869AC1?1&q=osian) |
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- [Arabic Wikipedia](https://archive.org/details/arwiki-20190201) |
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- The unshuffled version of the Arabic [OSCAR corpus](https://oscar-corpus.com/) |
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- DA (dialectal Arabic) |
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- A collection of dialectal Arabic data described in [our paper](https://arxiv.org/abs/2103.06678). |
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- CA (classical Arabic) |
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- [OpenITI (Version 2020.1.2)](https://zenodo.org/record/3891466#.YEX4-F0zbzc) |
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## Training procedure |
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We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training. |
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We follow the original English BERT model's hyperparameters for pre-training, unless otherwise specified. |
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### Preprocessing |
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- After extracting the raw text from each corpus, we apply the following pre-processing. |
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- We first remove invalid characters and normalize white spaces using the utilities provided by [the original BERT implementation](https://github.com/google-research/bert/blob/eedf5716ce1268e56f0a50264a88cafad334ac61/tokenization.py#L286-L297). |
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- We also remove lines without any Arabic characters. |
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- We then remove diacritics and kashida using [CAMeL Tools](https://github.com/CAMeL-Lab/camel_tools). |
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- Finally, we split each line into sentences with a heuristics-based sentence segmenter. |
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- We train a WordPiece tokenizer on the entire dataset (167 GB text) with a vocabulary size of 30,000 using [HuggingFace's tokenizers](https://github.com/huggingface/tokenizers). |
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- We do not lowercase letters nor strip accents. |
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### Pre-training |
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- The model was trained on a single cloud TPU (`v3-8`) for one million steps in total. |
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- The first 90,000 steps were trained with a batch size of 1,024 and the rest was trained with a batch size of 256. |
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- The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. |
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- We use whole word masking and a duplicate factor of 10. |
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- We set max predictions per sequence to 20 for the dataset with max sequence length of 128 tokens and 80 for the dataset with max sequence length of 512 tokens. |
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- We use a random seed of 12345, masked language model probability of 0.15, and short sequence probability of 0.1. |
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- The optimizer used is Adam with a learning rate of 1e-4, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. |
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## Evaluation results |
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- We evaluate our pre-trained language models on five NLP tasks: NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. |
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- We fine-tune and evaluate the models using 12 dataset. |
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- We used Hugging Face's transformers to fine-tune our CAMeLBERT models. |
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- We used transformers `v3.1.0` along with PyTorch `v1.5.1`. |
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- The fine-tuning was done by adding a fully connected linear layer to the last hidden state. |
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- We use \\(F_{1}\\) score as a metric for all tasks. |
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- Code used for fine-tuning is available [here](https://github.com/CAMeL-Lab/CAMeLBERT). |
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### Results |
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| Task | Dataset | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | |
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| -------------------- | --------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | |
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| NER | ANERcorp | MSA | 80.2% | 66.2% | 74.2% | 82.4% | 82.3% | 82.0% | 82.3% | 80.5% | |
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| POS | PATB (MSA) | MSA | 97.3% | 96.6% | 96.5% | 97.4% | 97.4% | 97.4% | 97.4% | 97.4% | |
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| | ARZTB (EGY) | DA | 90.1% | 88.6% | 89.4% | 90.8% | 90.3% | 90.5% | 90.5% | 90.4% | |
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| | Gumar (GLF) | DA | 97.3% | 96.5% | 97.0% | 97.1% | 97.0% | 97.0% | 97.1% | 97.0% | |
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| SA | ASTD | MSA | 76.3% | 69.4% | 74.6% | 76.9% | 76.0% | 76.8% | 76.7% | 75.3% | |
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| | ArSAS | MSA | 92.7% | 89.4% | 91.8% | 93.0% | 92.6% | 92.5% | 92.5% | 92.3% | |
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| | SemEval | MSA | 69.0% | 58.5% | 68.4% | 72.1% | 70.7% | 72.8% | 71.6% | 71.2% | |
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| DID | MADAR-26 | DA | 62.9% | 61.9% | 61.8% | 62.6% | 62.0% | 62.8% | 62.0% | 62.2% | |
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| | MADAR-6 | DA | 92.5% | 91.5% | 92.2% | 91.9% | 91.8% | 92.2% | 92.1% | 92.0% | |
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| | MADAR-Twitter-5 | MSA | 75.7% | 71.4% | 74.2% | 77.6% | 78.5% | 77.3% | 77.7% | 76.2% | |
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| | NADI | DA | 24.7% | 17.3% | 20.1% | 24.9% | 24.6% | 24.6% | 24.9% | 23.8% | |
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| Poetry | APCD | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | |
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### Results (Average) |
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| | Variant | Mix | CA | DA | MSA | MSA-1/2 | MSA-1/4 | MSA-1/8 | MSA-1/16 | |
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| -------------------- | ------- | ----- | ----- | ----- | ----- | ------- | ------- | ------- | -------- | |
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| Variant-wise-average<sup>[[1]](#footnote-1)</sup> | MSA | 81.9% | 75.3% | 79.9% | 83.2% | 82.9% | 83.1% | 83.0% | 82.1% | |
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| | DA | 73.5% | 71.1% | 72.1% | 73.5% | 73.1% | 73.4% | 73.3% | 73.1% | |
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| | CA | 79.8% | 80.9% | 79.6% | 79.7% | 79.9% | 80.0% | 79.7% | 79.8% | |
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| Macro-Average | ALL | 78.2% | 74.0% | 76.6% | 78.9% | 78.6% | 78.8% | 78.7% | 78.2% | |
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<a name="footnote-1">[1]</a>: Variant-wise-average refers to average over a group of tasks in the same language variant. |
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## Acknowledgements |
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This research was supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC). |
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## Citation |
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```bibtex |
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@inproceedings{inoue-etal-2021-interplay, |
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title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models", |
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author = "Inoue, Go and |
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Alhafni, Bashar and |
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Baimukan, Nurpeiis and |
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Bouamor, Houda and |
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Habash, Nizar", |
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booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", |
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month = apr, |
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year = "2021", |
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address = "Kyiv, Ukraine (Online)", |
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publisher = "Association for Computational Linguistics", |
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abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a mix of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", |
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} |
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``` |
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